


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


lowerCAmelCase			=				logging.get_logger(__name__)
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		def constraint_to_multiple_of(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=0		,							SCREAMING_SNAKE_CASE=None	):
				lowercase__				= round(val / multiple	) * multiple

				if max_val is not None and x > max_val:
						lowercase__				= math.floor(val / multiple	) * multiple

				if x < min_val:
						lowercase__				= math.ceil(val / multiple	) * multiple

				return x

		lowercase__				= (output_size, output_size) if isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	) else output_size

		lowercase__	,						lowercase__				= get_image_size(SCREAMING_SNAKE_CASE	)
		lowercase__	,						lowercase__				= output_size

		# determine new height and width
		lowercase__				= output_height / input_height
		lowercase__				= output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width	) < abs(1 - scale_height	):
						# fit width
						lowercase__				= scale_width
				else:
						# fit height
						lowercase__				= scale_height

		lowercase__				= constraint_to_multiple_of(scale_height * input_height		,							multiple=SCREAMING_SNAKE_CASE	)
		lowercase__				= constraint_to_multiple_of(scale_width * input_width		,							multiple=SCREAMING_SNAKE_CASE	)

		return (new_height, new_width)


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     Optional[Any]     =					['''pixel_values''']

							def __init__(				self:      Optional[Any]       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Dict[str, int] = None       ,		UpperCamelCase_:      PILImageResampling = PILImageResampling.BILINEAR       ,		UpperCamelCase_:      bool = False       ,		UpperCamelCase_:      int = 1       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Union[int, float] = 1 / 255       ,		UpperCamelCase_:      bool = True       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = None       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = None       ,		**UpperCamelCase_:      Tuple       ,		)     ->  None:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_				)
									lowercase__				= size if size is not None else {'''height''': 384, '''width''': 384}
									lowercase__				= get_size_dict(UpperCamelCase_				)
									lowercase__				= do_resize
									lowercase__				= size
									lowercase__				= keep_aspect_ratio
									lowercase__				= ensure_multiple_of
									lowercase__				= resample
									lowercase__				= do_rescale
									lowercase__				= rescale_factor
									lowercase__				= do_normalize
									lowercase__				= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase__				= image_std if image_std is not None else IMAGENET_STANDARD_STD

							def  lowerCamelCase_					(				self:      Any       ,		UpperCamelCase_:      np.ndarray       ,		UpperCamelCase_:      Dict[str, int]       ,		UpperCamelCase_:      bool = False       ,		UpperCamelCase_:      int = 1       ,		UpperCamelCase_:      PILImageResampling = PILImageResampling.BICUBIC       ,		UpperCamelCase_:      Optional[Union[str, ChannelDimension]] = None       ,		**UpperCamelCase_:      int       ,		)     ->  np.ndarray:





									"""simple docstring"""







									lowercase__				= get_size_dict(UpperCamelCase_				)
									if "height" not in size or "width" not in size:
											raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'				)
									lowercase__				= get_resize_output_image_size(
									    UpperCamelCase_       ,		output_size=(size['''height'''], size['''width'''])       ,		keep_aspect_ratio=UpperCamelCase_       ,		multiple=UpperCamelCase_       ,		)
									return resize(UpperCamelCase_       ,		size=UpperCamelCase_       ,		resample=UpperCamelCase_       ,		data_format=UpperCamelCase_       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Optional[Any]       ,		UpperCamelCase_:      np.ndarray       ,		UpperCamelCase_:      Union[int, float]       ,		UpperCamelCase_:      Optional[Union[str, ChannelDimension]] = None       ,		**UpperCamelCase_:      Any       ,		)     ->  List[Any]:





									"""simple docstring"""







									return rescale(UpperCamelCase_       ,		scale=UpperCamelCase_       ,		data_format=UpperCamelCase_       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      np.ndarray       ,		UpperCamelCase_:      Union[float, List[float]]       ,		UpperCamelCase_:      Union[float, List[float]]       ,		UpperCamelCase_:      Optional[Union[str, ChannelDimension]] = None       ,		**UpperCamelCase_:      Tuple       ,		)     ->  np.ndarray:





									"""simple docstring"""







									return normalize(UpperCamelCase_       ,		mean=UpperCamelCase_       ,		std=UpperCamelCase_       ,		data_format=UpperCamelCase_       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      ImageInput       ,		UpperCamelCase_:      bool = None       ,		UpperCamelCase_:      int = None       ,		UpperCamelCase_:      bool = None       ,		UpperCamelCase_:      int = None       ,		UpperCamelCase_:      PILImageResampling = None       ,		UpperCamelCase_:      bool = None       ,		UpperCamelCase_:      float = None       ,		UpperCamelCase_:      bool = None       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = None       ,		UpperCamelCase_:      Optional[Union[float, List[float]]] = None       ,		UpperCamelCase_:      Optional[Union[str, TensorType]] = None       ,		UpperCamelCase_:      ChannelDimension = ChannelDimension.FIRST       ,		**UpperCamelCase_:      List[Any]       ,		)     ->  PIL.Image.Image:





									"""simple docstring"""







									lowercase__				= do_resize if do_resize is not None else self.do_resize
									lowercase__				= size if size is not None else self.size
									lowercase__				= get_size_dict(UpperCamelCase_				)
									lowercase__				= keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
									lowercase__				= ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
									lowercase__				= resample if resample is not None else self.resample
									lowercase__				= do_rescale if do_rescale is not None else self.do_rescale
									lowercase__				= rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__				= do_normalize if do_normalize is not None else self.do_normalize
									lowercase__				= image_mean if image_mean is not None else self.image_mean
									lowercase__				= image_std if image_std is not None else self.image_std

									lowercase__				= make_list_of_images(UpperCamelCase_				)

									if not valid_images(UpperCamelCase_				):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

									if do_resize and size is None or resample is None:
											raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

									if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

									if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

									# All transformations expect numpy arrays.
									lowercase__				= [to_numpy_array(UpperCamelCase_				) for image in images]

									if do_resize:
											lowercase__				= [self.resize(image=UpperCamelCase_       ,		size=UpperCamelCase_       ,		resample=UpperCamelCase_				) for image in images]

									if do_rescale:
											lowercase__				= [self.rescale(image=UpperCamelCase_       ,		scale=UpperCamelCase_				) for image in images]

									if do_normalize:
											lowercase__				= [self.normalize(image=UpperCamelCase_       ,		mean=UpperCamelCase_       ,		std=UpperCamelCase_				) for image in images]

									lowercase__				= [to_channel_dimension_format(UpperCamelCase_       ,		UpperCamelCase_				) for image in images]

									lowercase__				= {'''pixel_values''': images}
									return BatchFeature(data=UpperCamelCase_       ,		tensor_type=UpperCamelCase_				)


							def  lowerCamelCase_					(				self:      List[Any]       ,		UpperCamelCase_:      str       ,		UpperCamelCase_:      List[Tuple] = None				)     ->  int:





									"""simple docstring"""







									lowercase__				= outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
											if len(UpperCamelCase_				) != len(UpperCamelCase_				):
													raise ValueError(
													    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''				)

											if is_torch_tensor(UpperCamelCase_				):
													lowercase__				= target_sizes.numpy()

											lowercase__				= []

											for idx in range(len(UpperCamelCase_				)				):
													lowercase__				= torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0				)       ,		size=target_sizes[idx]       ,		mode='''bilinear'''       ,		align_corners=UpperCamelCase_				)
													lowercase__				= resized_logits[0].argmax(dim=0				)
													semantic_segmentation.append(UpperCamelCase_				)
									else:
											lowercase__				= logits.argmax(dim=1				)
											lowercase__				= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

									return semantic_segmentation








'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				__magic_name__							,				__magic_name__							,				unittest.TestCase			):
   _a    						=   StableUnCLIPImgaImgPipeline
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _a    						=   frozenset(
       []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _a    						=   frozenset([]			)







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       32
     __a								=       embedder_hidden_size

     # image encoding components

     __a								=       CLIPImageProcessor(crop_size=32   ,				size=32   )

     torch.manual_seed(0   )
     __a								=       CLIPVisionModelWithProjection(
         CLIPVisionConfig(
             hidden_size=UpperCamelCase   ,				projection_dim=UpperCamelCase   ,				num_hidden_layers=5   ,				num_attention_heads=4   ,				image_size=32   ,				intermediate_size=37   ,				patch_size=1   ,				)   )

     # regular denoising components

     torch.manual_seed(0   )
     __a								=       StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase   )
     __a								=       DDPMScheduler(beta_schedule='squaredcos_cap_v2'   )

     torch.manual_seed(0   )
     __a								=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

     torch.manual_seed(0   )
     __a								=       CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=UpperCamelCase   ,				projection_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)   )

     torch.manual_seed(0   )
     __a								=       UNetaDConditionModel(
         sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')   ,				up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')   ,				block_out_channels=(32, 64)   ,				attention_head_dim=(2, 4)   ,				class_embed_type='projection'   ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2   ,				cross_attention_dim=UpperCamelCase   ,				layers_per_block=1   ,				upcast_attention=UpperCamelCase   ,				use_linear_projection=UpperCamelCase   ,				)

     torch.manual_seed(0   )
     __a								=       DDIMScheduler(
         beta_schedule='scaled_linear'   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				prediction_type='v_prediction'   ,				set_alpha_to_one=UpperCamelCase   ,				steps_offset=1   ,				)

     torch.manual_seed(0   )
     __a								=       AutoencoderKL()

     __a								=       {
         # image encoding components
         'feature_extractor': feature_extractor,
         'image_encoder': image_encoder.eval(),
         # image noising components
         'image_normalizer': image_normalizer.eval(),
         'image_noising_scheduler': image_noising_scheduler,
         # regular denoising components
         'tokenizer': tokenizer,
         'text_encoder': text_encoder.eval(),
         'unet': unet.eval(),
         'scheduler': scheduler,
         'vae': vae.eval(),
     }

     return components







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=0   ,				UpperCamelCase=True   )					->     Dict:
     if str(UpperCamelCase   ).startswith('mps'   ):
       __a								=       torch.manual_seed(UpperCamelCase   )
     else:
       __a								=       torch.Generator(device=UpperCamelCase   ).manual_seed(UpperCamelCase   )

     __a								=       floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(UpperCamelCase   )   ).to(UpperCamelCase   )

     if pil_image:
       __a								=       input_image * 0.5 + 0.5
       __a								=       input_image.clamp(0   ,				1   )
       __a								=       input_image.cpu().permute(0   ,				2   ,				3   ,				1   ).float().numpy()
       __a								=       DiffusionPipeline.numpy_to_pil(UpperCamelCase   )[0]

     return {
         "prompt": "An anime racoon running a marathon",
         "image": input_image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "np",
     }







   @skip_mps
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       'cpu'  # ensure determinism for the device-dependent torch.Generator
     __a								=       self.get_dummy_components()
     __a								=       StableUnCLIPImgaImgPipeline(**UpperCamelCase   )
     __a								=       sd_pipe.to(UpperCamelCase   )
     sd_pipe.set_progress_bar_config(disable=UpperCamelCase   )

     __a								=       self.get_dummy_inputs(UpperCamelCase   )
     inputs.update({'image_embeds': None}   )
     __a								=       sd_pipe(**UpperCamelCase   ).images
     __a								=       image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     __a								=       np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase   )







   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()   ,				reason='XFormers attention is only available with CUDA and `xformers` installed'   ,				)
   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase   )





@slow
@require_torch_gpu
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-l-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     __a								=       pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       pipe(
         UpperCamelCase   ,				'anime turtle'   ,				num_inference_steps=2   ,				output_type='np'   ,				)

     __a								=       torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9



